The use of personal pronoun in Inaugural Speeches

Part 0 - Topic introduction

People always use a lot of personal pronouns to avoid repeating someone’s names time and time again. Different people may use different personal pronouns for different weight in different situation to demonstrate different meanings and emotions. What about the situations in presidents’ Inaugural Speeches?

Part 1 - Packages preparaion

packages.used=c("RColorBrewer","qdap","ggplot2","reshape2","syuzhet","tm","qdap","reshape2","gridExtra","ggplot2","wordcloud","dplyr","tidytext")
# check packages that need to be installed.
packages.needed=setdiff(packages.used, 
                        intersect(installed.packages()[,1], 
                                  packages.used))
# install additional packages
if(length(packages.needed)>0){
  install.packages(packages.needed, dependencies = TRUE)
}
require(devtools,quietly = T)
install_github('recharts', 'taiyun')

Username parameter is deprecated. Please use taiyun/rechartsSkipping install of 'recharts' from a github remote, the SHA1 (9afee29c) has not changed since last install.
  Use `force = TRUE` to force installation

# library packages
library("RColorBrewer",quietly = T)
library("qdap",quietly = T)
library("ggplot2",quietly = T)
library("reshape2",quietly = T)
library("syuzhet",quietly = T)
library("tm",quietly = T)
library("qdap",quietly = T)
library("reshape2",quietly = T)
library("gridExtra",quietly = T)
library("ggplot2",quietly = T)
library("wordcloud",quietly = T)
library("dplyr",quietly = T)
library("tidytext",quietly = T)
library("recharts",quietly = T)
#part of codes are in this R file.
source("../lib/customized_function.R")

Part 2 - Data cleaning

We count the word frequency for each speeches and make this data matrix on which our whole analysis would based.

inaug.list=read.csv("../data/InaugurationInfo.csv", stringsAsFactors = FALSE)
inaug.data=read.table("../data/InauguationDates.txt", stringsAsFactors = FALSE,blank.lines.skip=F,sep= "\t")
mat1<-NULL
for(i in seq(nrow(inaug.list))) {
filename <- paste0("../data/InauguralSpeeches/inaug", 
                     inaug.list$File[i], "-", 
                     inaug.list$Term[i], ".txt")
tx <- readLines(filename,warn=F)
if (i==58) 
{
  tx<-paste(tx, collapse = " ")
  tx_words <- strsplit(tx, split = " ")[[1]]
}
else
tx_words <- strsplit(tx, split = " ")[[1]]
date_pattern2 <- "[a-zA-z]+"
tx_words2 <- grep(tx_words, pattern = date_pattern2, value=T)
tx_log <- grepl(tx_words2, pattern = date_pattern2)
matches <- gregexpr(pattern = date_pattern2, text = tx_words2[tx_log])
tx_words <- unlist(regmatches(tx_words2[tx_log], matches))
tx_words <- unlist(rm_stopwords(tx_words))
mat<-NULL
table<-table(tx_words)
word<-names(table)
n<-unname(table)
matname<-paste(inaug.list$File[i],inaug.list$Term[i])
mat<-cbind(rep(matname,nrow(n)),word,n)
mat1<-rbind(mat1,mat)
}
colnames(mat1)<-c("name","word","n") 
matsort<-mat1[order(as.numeric(mat1[,3]),decreasing = T),]
head(matsort)

     name                     word    n    
[1,] "WilliamHenryHarrison 1" "which" "107"
[2,] "WilliamHenryHarrison 1" "by"    "103"
[3,] "JamesKPolk 1"           "our"   "101"
[4,] "CalvinCoolidge 1"       "we"    "88" 
[5,] "WilliamHenryHarrison 1" "their" "81" 
[6,] "WarrenGHarding 1"       "we"    "80" 

Part 3 - Different president uses personal pronoun for different weight

We divide personal pronouns into 4 types,first person singular form,first person plural form,second person form and third person form.

#personal pronoun
first1<-c("I","me","mine","my")
first2<-c("we","our","ours","us")
second<-c("you","your","yours")
third<-c("he","she","it","him","her","his","hers","they","them","their")
personal<-list(first1,first2,second,third)
pp_mat<-matrix(nrow = nrow(inaug.list),ncol = 4)
for(i in seq(nrow(inaug.list))) {
  test<-matsort[matsort[,"name"]==paste(inaug.list$File[i],inaug.list$Term[i]),"word"]
  pp_mat[i,]<-find_personal_pronoun(test)
}
ppname<-NULL
for(i in seq(nrow(inaug.list))) {
ppname[i] <- paste(inaug.list$File[i], inaug.list$Term[i]) }
rownames(pp_mat)<-ppname
colnames(pp_mat)<-c("first1","first2","second","third")
head(pp_mat)

                      first1    first2    second     third
GeorgeWashington 1 0.2222222 0.3333333 0.1111111 0.3333333
GeorgeWashington 2 0.6666667 0.0000000 0.3333333 0.0000000
JohnAdams 1        0.2222222 0.4444444 0.1111111 0.2222222
ThomasJefferson 1  0.2222222 0.3333333 0.1111111 0.3333333
ThomasJefferson 2  0.2500000 0.3750000 0.1250000 0.2500000
JamesMadison 1     0.2857143 0.4285714 0.0000000 0.2857143

pp_mat1<-pp_mat
rownames(pp_mat1)<-seq(1789,2017,4)
pp_mat2<-melt(pp_mat1)
pp_mat3<-melt(pp_mat)
new1<-mat2mat(pp_mat)
a<-ggplot(data.frame(personal_pronoun=new1[,"type"],new1[,"name"],new1[,"prop"]), aes(x=factor(new1[,"name"], levels=unique(new1[,"name"])),y=new1[,"prop"], colour=personal_pronoun,group=personal_pronoun))+ geom_line(size=2) + theme(axis.text.x = element_blank()) + xlab("time with different presidents") + ylab("proportion") 
pp_dat<-data.frame(year=pp_mat2$Var1,proportion=pp_mat2$value,personal_pronoun=pp_mat2$Var2)
b<-ggplot(pp_dat, aes(x = year, y = proportion, fill = personal_pronoun)) +
  geom_area() +
  scale_fill_brewer(palette = "Blues", breaks = rev(c("third","second","first2","first1")))
pp_dat<-data.frame(president=pp_mat3$Var1,proportion=pp_mat2$value,personal_pronoun=pp_mat2$Var2)
c<-ggplot(pp_dat,aes(president,proportion,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+
  scale_fill_brewer(palette = "Blues")+ theme(axis.text.x = element_blank()) + xlab("time with different presidents")
#grid.arrange(b,c,nrow=2)

Notice:The plot(and some following plots) are interactive by moving your mouse to a certain data point,details can be shown.

3.1 General Trend

eArea(pp_mat1)

In general

• There is no obvious trend,just some fluctuations for second & third person using weight.
• There is a slightly decreasing for first person singular form using weight.
• There is a slightly increasing for first person plural form using weight.

It’s quite reasonable,since presidents come to realize that they should emphasize people’s rights and responsibilities to let people feel that they are being together,the interests of every citizens are closely related to destiny of the United States,rather than just talking about themselves.

3.2 Comparing between different terms

#different term 
avg<-apply(pp_mat,2,mean)
list<-term_sep()
avg_term1<-apply(pp_mat[list[[1]],],2,mean)
avg_term2<-apply(pp_mat[list[[2]],],2,mean)
df1<-data.frame(avg,avg_term1,avg_term2)
dat<-t(df1)
mydat<-melt(dat)
#mydat
mydat<-data.frame(term = mydat$Var1,personal_pronoun = mydat$Var2,proportion=round(mydat$value,2))
ggplot(mydat,aes(term,proportion,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+theme(axis.ticks.length=unit(0.5,'cm'))+guides(fill=guide_legend(title=NULL))+
  scale_fill_brewer(palette = "Blues")

eRadar(xtabs( proportion~ term+personal_pronoun, mydat))

From president’s term 1 to term 2,they tend to use more first person plural form than first person singular form.It’s also reasonable, since the president and the public become familiar to each other,so the president naturally take himself and the public as a whole group.

3.3 Comparing among different parties

#different party
new_inaug.list<-cbind(inaug.list,pp_mat)
first1<-tapply(new_inaug.list$first1,new_inaug.list$Party,mean)
first2<-tapply(new_inaug.list$first2,new_inaug.list$Party,mean)
second<-tapply(new_inaug.list$second,new_inaug.list$Party,mean)
third<-tapply(new_inaug.list$third,new_inaug.list$Party,mean)
#head(new_inaug.list)
#aggregate(new_inaug.list,)
dat<-rbind(unname(first1),unname(first2),unname(second),unname(third))
colnames(dat)<-names(first1)
rownames(dat)<-c("first1","first2","second","third")
mydat<-melt(dat)
mydat<-data.frame(personal_pronoun = mydat$Var1, party = mydat$Var2,proportion=round(mydat$value,2))
ggplot(mydat,aes(party,proportion,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+theme(axis.ticks.length=unit(0.5,'cm'))+guides(fill=guide_legend(title=NULL))+
  scale_fill_brewer(palette = "Blues")+ theme(axis.text.x = element_text(angle = 15, hjust = 0.5, vjust = 0.5))

eRadar(xtabs( proportion~ party+personal_pronoun, mydat))

As the plot shown,Whig used first person singular form and third person most frequently,used first person plural form as well as second person at least frequency.While federalist is almost the opposite.

Part 4 - Analysis on each kind of personal pronoun’s CRAZY FAN

data1<-pp_mat[order(pp_mat[,1],decreasing = T),][1:10,]
data2<-pp_mat[order(pp_mat[,2],decreasing = T),][1:10,]
data3<-pp_mat[order(pp_mat[,3],decreasing = T),][1:10,]
data4<-pp_mat[order(pp_mat[,4],decreasing = T),][1:10,]
new1<-mat2mat(data1)
new2<-mat2mat(data2)
new3<-mat2mat(data3)
new4<-mat2mat(data4)
new1<-data.frame(type=new1[,"type"],name=new1[,"name"],proportion=as.numeric(new1[,"prop"]))
new2<-data.frame(type=new2[,"type"],name=new2[,"name"],proportion=as.numeric(new2[,"prop"]))
new3<-data.frame(type=new3[,"type"],name=new3[,"name"],proportion=as.numeric(new3[,"prop"]))
new4<-data.frame(type=new4[,"type"],name=new4[,"name"],proportion=as.numeric(new4[,"prop"]))

4.1 Top ten using first person singular form

eBar(new1, ~name, ~proportion, ~type)

test<-topic_wc(2,1)
dat<-data.frame(word=test[,"word"],freq=5*as.numeric(test[,"n"]))
#par(mfrow=c(1,2))
eWordcloud(dat[7:65,], namevar = ~word, datavar = ~freq,size = c(600, 600),title = "George Washington & first person singular",rotationRange = c(-1, 1))

test<-topic_wc(2,3)
dat<-data.frame(word=test[,"word"],freq=5*as.numeric(test[,"n"]))
eWordcloud(dat[2:32,], namevar = ~word, datavar = ~freq,size = c(600, 600),title = "George Washington & second person",rotationRange = c(-1, 1))

No.1 “first person singular user” is George Washington(term 2).The 67% personal pronoun George Washington used in his second person is first person singular form. He also used second person quite frequently at about 33%.Therefore,we can speculate in his speeches,he set he and the listeners apart,taking himself as a leader talking something like “administration”,“chief”, while using second term to express his anticipation to public.

test<-matsort[matsort[,"name"] == "WilliamMcKinley 1",]
test1<-matsort[matsort[,"name"] == "WoodrowWilson 2",]
test2<-matsort[matsort[,"name"] == "JamesBuchanan 1",]
test<-rbind(test,test1,test2)
test<-test[order(as.numeric(test[,3]),decreasing = T),]
test<-test[17:2529,]
dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#par(mfrow=c(1,2))
eWordcloud(dat, namevar = ~word, datavar = ~freq,size = c(600, 600),title = "WilliamMcKinley 1 & WoodrowWilson 2 & JamesBuchanan 1",rotationRange = c(-1, 1))

Except Washington,from No.2 William McKinley(term 1) to No.4 James Buchanan(term 1),they didn’t even use second person.Add their three’s speeches to make the wordcloud,we can hardly find the words demonstrating centralization of power, instead,there are words like “congress”,“fair”,“civil”,“life”,showing more gentle emotion.

4.2 Top ten using first person plural form

eBar(new2, ~name, ~proportion, ~type)

test<-matsort[matsort[,"name"] == "FranklinDRoosevelt 3",]
test1<-matsort[matsort[,"name"] == "TheodoreRoosevelt 1",]
test2<-matsort[matsort[,"name"] == "AndrewJackson 1",]
test3<-matsort[matsort[,"name"] == "AbrahamLincoln 2",]
test4<-matsort[matsort[,"name"] == "WilliamMcKinley 2",]
test5<-matsort[matsort[,"name"] == "FranklinDRoosevelt 4",]
test<-rbind(test,test1,test2,test3,test4,test5)
test<-test[order(as.numeric(test[,3]),decreasing = T),]
test<-test[40:2638,]
dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#par(mfrow=c(1,2))
eWordcloud(dat, namevar = ~word, datavar = ~freq,size = c(600, 600),title = "TOP 6 using first person plural form",rotationRange = c(-1, 1))

For presidents frequently using first person plural form, there exists similar phenomenon.From No.1 Franklin DRoosevelt(term 3) to No.6 Franklin DRoosevelt(term 4),they also make the speeches without using second person.Add their speeches to make the wordcloud,we find similar words showing gentle emotion.

4.3 Top ten using second person

eBar(new3, ~name, ~proportion, ~type)

4.4 Top ten using third person

eBar(new4, ~name, ~proportion, ~type)

4.5 Each personal pronoun’s “Fan Club” feature : positive? negative?

data(positive.words)
data(negative.words)
sentiment_analy<-function(words)
{
      score<-NULL
      # compare words to the dictionaries of positive & negative terms
      positive.matches = match(words, positive.words)
      negative.matches = match(words, negative.words)
      # get the position of the matched term or NA
      # we just want a TRUE/FALSE
      positive_matches = !is.na(positive.matches)
      negative_matches = !is.na(negative.matches)
      score = sum(positive_matches) - sum(negative_matches) 
      return(score)
}
senti_vec<-NULL
for(i in seq(nrow(inaug.list))) {
  test<-matsort[matsort[,"name"]==paste(inaug.list$File[i],inaug.list$Term[i]),"word"]
  senti_vec[i]<-sentiment_analy(test)
}
svname<-NULL
for(i in seq(nrow(inaug.list))) {
svname[i] <- paste(inaug.list$File[i], inaug.list$Term[i]) }
senti_vec

#sentiment vs person
senti_mat<-cbind(ppname,senti_vec)
n1<-match(rownames(data1),ppname)
n2<-match(rownames(data2),ppname)
n3<-match(rownames(data3),ppname)
n4<-match(rownames(data4),ppname)
senti_mat2<-cbind(senti_mat[n1,2],rep("first1",10))
senti_mat2<-rbind( senti_mat2 , cbind(senti_mat[n2,2],rep("first2",10)) )
senti_mat2<-rbind( senti_mat2 , cbind(senti_mat[n3,2],rep("second",10)) )
senti_mat2<-rbind( senti_mat2 , cbind(senti_mat[n4,2],rep("third",10)) )
colnames(senti_mat2)<-c("sentimental_scores","personal_pron")
dat<-data.frame(senti_mat2)
p <- ggplot(dat, aes(personal_pron, as.numeric(sentimental_scores)))
p + geom_boxplot()

According to the plot, First person plural’s “Fan Club” tend to be more negative,while the third person form’s “Fan Club” seem to be more positive. But there is no obvious differences.

4.6 Each personal pronoun’s “Fan Club” feature : average of word using times?

#avg time vs person
avg_mat<-matrix(nrow = nrow(inaug.list),ncol = 2)
avg_mat[,1]<-ppname
for(i in seq(nrow(inaug.list))) {
  test<-matsort[matsort[,"name"]==paste(inaug.list$File[i],inaug.list$Term[i]),]
  avg_mat[i,2]<-sum(as.numeric(test[,3]))/nrow(test)
}
senti_mat3<-cbind(avg_mat[n1,2],rep("first1",10))
senti_mat3<-rbind( senti_mat3 , cbind(avg_mat[n2,2],rep("first2",10)) )
senti_mat3<-rbind( senti_mat3 , cbind(avg_mat[n3,2],rep("second",10)) )
senti_mat3<-rbind( senti_mat3 , cbind(avg_mat[n4,2],rep("third",10)) )
colnames(senti_mat3)<-c("word_avg_time","personal_pron")
dat<-data.frame(senti_mat3)
p <- ggplot(dat, aes(personal_pron, as.numeric(word_avg_time)))
p + geom_boxplot()

According to the plot, First person plural’s “Fan Club” tend to use the same words for less times,the median is about 12 times;while the third person form’s “Fan Club” using the same words for more times,the median is about 27 times.

Part 5 - What are they talking/feeling using personal pronoun ?

Finding 1:first singular form always come with trust and anticipation.

• George Washington

senten_mat2<-melt(senten_mat(2))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2,2)
senti_mat<-round(count_emo(2)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat)<-seq(1,nrow(senti_mat))
senti_mat2<-melt(senti_mat)
senti_dat<-data.frame(sentence_number=senti_mat2$Var1,score=senti_mat2$value,sentiment=senti_mat2$Var2)
#par(mfrow=c(1,2))
a1<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+ scale_fill_brewer(palette = "Blues")+ xlab("")
#b1<-ggplot(senti_dat, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
# + scale_fill_brewer(palette = "RdBu", breaks = rev(c(factor(sentiment))))
#b1<-ggplot(senti_dat,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
#grid.arrange(a1,b1,nrow=2)
a1

eArea(senti_mat)

#find topics
#test<-topic_wc(2,1)
#test<-test[7:65,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq,size = c(600, 600),title = "George Washington & fist person singular",rotationRange = c(-1, 1))

• William McKinley

senten_mat2<-melt(senten_mat(28))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2,2)
senti_mat<-round(count_emo(28)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat)<-seq(1,nrow(senti_mat))
senti_mat2<-melt(senti_mat)
senti_dat<-data.frame(sentence_number=senti_mat2$Var1,score=senti_mat2$value,sentiment=senti_mat2$Var2)
#par(mfrow=c(1,2))
a1<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+ scale_fill_brewer(palette = "Blues")+ xlab("")
#b1<-ggplot(senti_dat, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
# + scale_fill_brewer(palette = "RdBu", breaks = rev(c(factor(sentiment))))
#b1<-ggplot(senti_dat,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
#grid.arrange(a1,b1,nrow=2)
a1

eArea(senti_mat)

#find_sen(28,134)
#find_sen(28,135)
#find_sen(28,136)
#find_sen(28,137)
#test<-topic_wc(28,1)[1:60,]
#test<-test[6:60,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq,size = c(600, 600),title = "WilliamMcKinley & fist person singular",rotationRange = c(-1, 1))

Finding 2 :first plural form always convey positive emotions.

• Richard Nixon

senten_mat2<-melt(senten_mat(45))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2)
par(mfrow=c(4,1), mar=c(1,0,2,0), bty="n", xaxt="n", yaxt="n", font.main=1)
a2<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+
  scale_fill_brewer(palette = "Blues")+ xlab("")
senti_mat3<-round(count_emo(45)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat3)<-seq(1,nrow(senti_mat3))
senti_mat4<-melt(senti_mat3)
senti_dat2<-data.frame(sentence_number=senti_mat4$Var1,score=senti_mat4$value,sentiment=senti_mat4$Var2)
#b2<-ggplot(senti_dat2, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
#b2<-ggplot(senti_dat2,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
a2

apply(senti_mat3,2,mean)

       anger anticipation         fear          joy     surprise        trust 
  0.01402062   0.03164948   0.03309278   0.03288660   0.01072165   0.05123711 

eArea(senti_mat3)

#test<-topic_wc(45,2)[1:60,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq)

• Barack Obama

senten_mat2<-melt(senten_mat(57))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2,2)
senti_mat<-round(count_emo(57)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat)<-seq(1,nrow(senti_mat))
senti_mat2<-melt(senti_mat)
senti_dat<-data.frame(sentence_number=senti_mat2$Var1,score=senti_mat2$value,sentiment=senti_mat2$Var2)
#par(mfrow=c(1,2))
a1<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+ scale_fill_brewer(palette = "Blues")+ xlab("")
#b1<-ggplot(senti_dat, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
# + scale_fill_brewer(palette = "RdBu", breaks = rev(c(factor(sentiment))))
#b1<-ggplot(senti_dat,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
#grid.arrange(a1,b1,nrow=2)
a1

eArea(senti_mat)

#find_sen(28,134)
#find_sen(28,135)
#find_sen(28,136)
#find_sen(28,137)
apply(senti_mat,2,mean)

       anger anticipation         fear          joy     surprise        trust 
  0.01561905   0.03638095   0.02866667   0.03800000   0.01390476   0.04952381 

#test<-topic_wc(35,2)[1:60,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq)

Finding 3 : Anger often begin with third person form.

• Ulysses SGrant

senten_mat2<-melt(senten_mat(21))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2)
a3<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+ scale_fill_brewer(palette = "Blues")+ xlab("")
senti_mat5<-round(count_emo(21)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat5)<-seq(1,nrow(senti_mat5))
senti_mat6<-melt(senti_mat5)
senti_dat3<-data.frame(sentence_number=senti_mat6$Var1,score=senti_mat6$value,sentiment=senti_mat6$Var2)
b3<-ggplot(senti_dat3,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
#b3<-ggplot(senti_dat3, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
a3

eArea(senti_mat5)

apply(senti_mat5,2,mean,na.rm=T)

       anger anticipation         fear          joy     surprise        trust 
 0.011818182  0.022045455  0.018636364  0.015909091  0.005227273  0.057045455 

#test<-topic_wc(57,2)[1:60,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq)

• Dwight DEisenhower

senten_mat2<-melt(senten_mat(43))
sen_dat<-data.frame(sentence.number=senten_mat2$Var1,times=senten_mat2$value,personal_pronoun=senten_mat2$Var2)
a3<-ggplot(sen_dat,aes(sentence.number,times,fill=personal_pronoun))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))+ scale_fill_brewer(palette = "Blues")+ xlab("")
senti_mat5<-round(count_emo(43)[,c(1,2,4,5,7,8)],2)
rownames(senti_mat5)<-seq(1,nrow(senti_mat5))
senti_mat6<-melt(senti_mat5)
senti_dat3<-data.frame(sentence_number=senti_mat6$Var1,score=senti_mat6$value,sentiment=senti_mat6$Var2)
b3<-ggplot(senti_dat3,aes(x = sentence_number, y = score, fill = sentiment))+geom_bar(stat="identity",position="stack")+ggtitle("")+guides(fill=guide_legend(title=NULL))
#b3<-ggplot(senti_dat3, aes(x = sentence_number, y = score, fill = sentiment)) +
#  geom_area() 
a3

eArea(senti_mat5)

apply(senti_mat5,2,mean,na.rm=T)

       anger anticipation         fear          joy     surprise        trust 
  0.01704082   0.04326531   0.03316327   0.04204082   0.01255102   0.08020408 

#test<-topic_wc(57,2)[1:60,]
#dat<-data.frame(word=test[,"word"],freq=test[,"n"])
#eWordcloud(dat, namevar = ~word, datavar = ~freq)

Part 6 - Next step

• Collect more data for other formal speeches to gain more convincing analysis.

• Smooth the emotion function to make it more clear.